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Updated: Jun 25, 2026

Modeling Hepatitis B Virus Infection in Non-Hepatic 293T-NE-3NRs Cells
09:02

Modeling Hepatitis B Virus Infection in Non-Hepatic 293T-NE-3NRs Cells

Published on: June 5, 2020

Modelling Hepatitis B virus related hospital discharges in Spain: ARIMAX based liver disease forecasting tool for

Lesly Acosta1, Núria Soldevila2,3, Núria Torner2

  • 1Universitat Politècnica de Catalunya· Barcelona Tech, Barcelona, Spain.

Plos One
|June 23, 2026
PubMed

Insights

Forecasting chronic hepatitis B (CHB) related cirrhosis and hepatocellular carcinoma (HCC) hospitalizations using ARIMA and ARIMAX models aids disease monitoring and resource allocation. ARIMAX models offer more accurate predictions for public health decisions.

Area of Science:

  • Epidemiology
  • Time Series Analysis
  • Public Health

Background:

  • Chronic hepatitis B (CHB) infection is a significant cause of cirrhosis and hepatocellular carcinoma (HCC).
  • Accurate forecasting of CHB-related hospitalizations is crucial for healthcare planning and intervention assessment.

Purpose of the Study:

  • To develop and evaluate ARIMA-based models for forecasting global hospital discharges related to CHB-induced cirrhosis and HCC.
  • To compare the predictive accuracy of seasonal ARIMA and ARIMAX models.

Main Methods:

  • Retrospective observational study of monthly CHB hospitalization data in Spain (2005-2021).
  • Utilized Spanish Minimum dataset of hospital discharge registry (CMBDH) with ICD codes for CHB, cirrhosis, and HCC.
  • Employed seasonal ARIMA and ARIMAX models, with data transformation for stationarity and model selection based on AIC, BIC, and MAPE.

Main Results:

  • A total of 6743 discharges were analyzed; 58% were HCC-related and 42% cirrhosis-related.
  • The best-fit model for CHB and HCC time series forecasting was an approximate Gaussian [Formula: see text]-ARIMAX (6,0,0) (0,1,1)_12 model.
  • The model effectively handled outliers, removed seasonal patterns, and captured autoregressive dynamics.

Conclusions:

  • ARIMA and ARIMAX models are vital for forecasting CHB-related cirrhosis and HCC, improving disease surveillance and resource management.
  • ARIMAX models provide more accurate, context-aware predictions, supporting informed public health decision-making.
Abstract

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